How AI Adoption Differs From Analytics Adoption?

The artificial intelligence (AI) business has regularly dominated the news over the last few years, and it is still underutilized. Having found the newfound potential in AI and how it is changing various sectors worldwide, businesses are now scrambling to figure out integration possibilities with new technology. Artificial intelligence is an idea that has been around for a long time. However, what’s multiplied its effectiveness is the astronomical rise in the cloud-based service possibilities and the power of computing overtime.

For instance, the influence of AI on marketing is expected to grow to approximately $40 billion by 2025. Although most CMOs have a fair awareness of AI, many doubt the size of the benefits & how they can adopt AI to boost marketing. But for any organization to commit to and accomplish a full-proof AI adoption, it should understand the differences and opportunities between predictive analytics, data analytics, and AI machine learning.

AI vs Analytics- An Overview

Most of the Analytics programs are more about keeping the “lights on” for the businesses. Although most of these Analytics programs are kind of an off-the-shelves product, it still requires effort from the organizations to blend these programs to integrate them with their processes. This is needed to ensure the higher adaptability of these analytics solutions.

Artificial Intelligence or AI, on the other hand, is looked upon by organizations as a driver of business, as a frontier that can usher their business to the AI age, which is probably going to come sooner than we had thought!

It can be said that the Analytics solutions have been commercialized lately. However, the interaction of such solutions with the client touch-points(organization processes) tends to be more limited. It begins with the analytics team understanding what the business is looking to do and then going ahead with a planned execution.

Artificial Intelligence requires a more hands-on approach with regular interaction with the stakeholders playing a key role. It is further iterative in nature. It requires quick market tests to find out how it is responding and as a process, it demands involvement. What’s more, the AI development team has to provide a fair idea of the time investment, the roadmap, and the IT personnel required to implement AI successfully. If data analytics is termed as the pixel, AI is the bigger picture!

Data Analytics Adoption vs AI Adoption- An In-depth Discussion

Data Analytics Adoption

Ever since its advent, managers have loved data analytics. They have benefited from a brief and impactful insight into heaps of data at their fingertips on dashboards. From consumer-tracking data on applications and websites to CRM data analysis and online advertising click-throughs, data analytics encompasses it all.

Data mining results in huge volumes of data, which are mostly unstructured. Marketers, however, are familiar with data interaction through dashboards that offer structured data to produce an analysis of commonalities like ratios, averages, and percentages. The end goal is to get a report, seek a relevant pattern, and discover relationships between variables.

While humans make assumptions, the data is queried by the analytics model to attest historical proof to that assumption. If valid, the testing can be scaled up on further data.

For instance, let’s say that a bank(let’s say X Bank) is running a loyalty program for its credit card. The analytics data found out that it has 5,000 aged male members, and 1500+ among them have redeemed their loyalty points for medicines. It will indicate that aged males are more likely to buy medicines, and thus the X Bank’s marketing efforts can be focused on healthcare for this segment. Such data Analysis is descriptive in nature being dependent on historical events. It fails to predict the influence of an alteration in a variable.

AI Adoption

AI combines many technologies and Machine Learning is at its core. Machine learning is an extension of the techniques employed by predictive analytics, with one major difference. Well, the AI system can make assumptions, test them, and learn on its own. Not only can AI establish assumptions but it can also reassess the data and re-evaluate the model by factoring in many variations. AI, coupled with machine learning is capable of testing & retesting data to forecast every imaginable customer-product match at lightning speed & capability that no human can achieve.

For instance, take the example of the X Bank’s credit card loyalty program again. With the implementation of AI, it is possible to dig further and develop workflows to identify the medicine shopping patterns of the customers & nudge them at the right time knocking with appropriate offers. The workflow & models can gradually be further optimized iteratively, depending on whether the customers to whom the offer was sent acted upon it or not.

Complex analysis, like the example shown above, may be performed in real-time with several more variables, facilitating the system to learn fast. This can provide micro-targeted insights that would be impossible for human analysts to offer across a huge population. These outcomes can vastly enhance ROI, conversion rates, and client loyalty significantly.